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arxiv: 2605.20701 · v1 · pith:RX7RHQ2Vnew · submitted 2026-05-20 · 💻 cs.HC · cs.MA

CandorMD: An AI-Assisted Audio Simulation and Feedback System for Training Clinicians for Medical Error Disclosure

Inna Wanyin Lin , Sahand Sabour , Hong Sng , Maxine Chan , Minlie Huang , Andrew White , Tim Althoff This is my paper

Pith reviewed 2026-05-21 04:04 UTC · model grok-4.3

classification 💻 cs.HC cs.MA
keywords AI simulationmedical error disclosureclinician trainingfeedback systemmedical communicationsimulation traininghuman-computer interaction
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0 comments X

The pith

CandorMD offers AI simulations for clinicians to practice disclosing medical errors with real-time feedback.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Clinicians struggle with disclosing medical errors because of emotional demands and training limited to lectures or non-adaptive videos. The paper presents CandorMD as an AI-assisted audio simulation system built to deliver immediate practice, tailored scenarios, and specific feedback matched to each user's needs. Semi-structured interviews with physicians, risk managers, patient advocates, and communication experts revealed current practices and gaps, which shaped the system's features. The work ends with design recommendations for future AI tools in medical communication training.

Core claim

The authors designed CandorMD as an AI-assisted audio simulation and feedback system that supplies real-time practice, actionable feedback, and diverse environments tailored to individual learning needs, after gathering input through stakeholder interviews on existing disclosure training shortfalls.

What carries the argument

CandorMD, the AI-assisted audio simulation system that supplies real-time practice and personalized feedback for medical error disclosure conversations.

Load-bearing premise

That interviews with stakeholders alone confirm the AI simulation will improve disclosure skills without any direct tests of learning outcomes.

What would settle it

A controlled comparison of how often clinicians avoid error disclosures or how patients rate the conversations, measured before and after training with CandorMD versus standard methods.

Figures

Figures reproduced from arXiv: 2605.20701 by Andrew White, Hong Sng, Inna Wanyin Lin, Maxine Chan, Minlie Huang, Sahand Sabour, Tim Althoff.

Figure 1
Figure 1. Figure 1: CandorMD core components. CandorMD is an audio-based simulation and feedback prototype for practicing medical [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Simulation and feedback pipeline in CandorMD. In [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Landing page of CandorMD [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: Simulation and turn-by-turn feedback page before [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: During conversation. Left panel shows conversa [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
read the original abstract

Clinicians are expected to disclose harmful medical errors to patients and families in line with ethical, regulatory, and patient care standards, yet these conversations remain challenging because of their emotional complexity and limited training opportunities. Most physicians still learn primarily through lectures and observation, while static video tools-though available-are underused, lack adaptability across specialties, and deliver delayed, generic feedback. These gaps restrict skill development, reduce self-efficacy, and contribute to avoidance of disclosure conversations, ultimately compromising patient care and eroding trust. To address these needs, we designed CandorMD -- an AI-assisted simulation system that provides real-time practice, actionable feedback, and diverse practice environments tailored to individual learning needs. We conducted semi-structured interviews with physicians, risk managers, patient advocates, and communication experts to understand current practices, identify gaps, and collect feedback on CandorMD. Based on these insights, we present findings and design recommendations for the future of AI-supported medical communication training.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper presents the design of CandorMD, an AI-assisted audio simulation and feedback system intended to train clinicians in disclosing harmful medical errors. It identifies limitations in current approaches (lectures, observation, static videos) and describes how the system supplies real-time practice, actionable feedback, and tailored environments. The design is grounded in semi-structured interviews with physicians, risk managers, patient advocates, and communication experts, from which the authors derive findings and design recommendations for AI-supported medical communication training.

Significance. If the interview-derived design recommendations prove robust, the work could usefully inform development of adaptive simulation tools for high-stakes, emotionally complex medical conversations. The qualitative stakeholder input is a clear strength, supplying concrete needs and gaps that future systems can target. Because the manuscript advances no efficacy claims or outcome measures, its contribution is primarily conceptual and design-oriented rather than demonstrative of improved clinician performance.

minor comments (3)
  1. [Abstract] Abstract: the claim that CandorMD 'provides real-time practice, actionable feedback, and diverse practice environments' is presented without any accompanying description of the underlying AI models, speech recognition pipeline, or feedback-generation logic; adding a brief technical overview in the system-design section would clarify feasibility.
  2. [Methods / Interview Study] Interview methodology: the manuscript does not report the number of participants, recruitment strategy, interview protocol, or analysis method (e.g., thematic analysis steps); these details are needed to let readers evaluate the strength of the resulting design recommendations.
  3. [System Design] Figure or system diagram: if a visual of the CandorMD interface or feedback loop exists, ensure it is referenced explicitly in the text and includes labels for the AI components so the 'actionable feedback' mechanism is traceable.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and constructive review of our manuscript on CandorMD. We appreciate the recognition that the qualitative stakeholder input is a clear strength and that the work makes a conceptual and design-oriented contribution. The recommendation for minor revision is noted, and we are prepared to incorporate any specific suggestions. No major comments were provided in the report, so we have no point-by-point revisions to detail at this stage.

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained design work

full rationale

The paper presents a system-design and qualitative interview study. Its central claim is that CandorMD was designed to supply real-time practice, actionable feedback, and tailored environments, with stakeholder interviews used to surface needs, gaps, and design recommendations. No equations, fitted parameters, predictions, or self-referential derivations appear. The argument does not reduce any result to its own inputs by construction, nor does it rely on load-bearing self-citations or uniqueness theorems. The work is descriptive and interview-driven; therefore the derivation chain is independent of the target claims and receives score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a qualitative design and interview study; it introduces no mathematical free parameters, no formal axioms, and no new postulated entities such as particles or forces.

pith-pipeline@v0.9.0 · 5722 in / 1137 out tokens · 34774 ms · 2026-05-21T04:04:39.383688+00:00 · methodology

discussion (0)

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    Do not include any additional fields

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    FOCUS ONLY ON THE MOST RECENT PHYSICIAN MESSAGE for your feedback and phrase identification

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    Only identify specific phrases from the CURRENT physician message that support your feedback Feedback Structure : Overall Score : Provide total score out of 5 for each criterion Strengths (1 -2 bullet points ) : Highlight what the physician did particularly well in their most recent message Priority Improvement Areas : Provide 1 -2 key areas for improveme...

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    IS - Information Sharing / Incident Acknowledgement & Explanation

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Showing first 80 references.